Energy-efficiency-oriented scheduling in smart manufacturing

Process systems engineering is one of the many areas in which mixed integer optimisation formulations have been successfully applied. The nature of the problems requires specialised solution strategies and computer packages or callable libraries able to be extended and modified in order to accommodate new solution techniques. Object-oriented programming languages have been identified to offer these features. Process system applications are usually of large scale, and require modelling and solution techniques with high level of customisation. ooMILP is a library of C++ callable procedures for the definition, manipulation and solution of large, sparse mixed integer linear programming (MILP) problems without the disadvantages of many existing modelling languages. We first present a general approach to the packaging of numerical solvers as software components, derived from material developed for the CAPE-OPEN project. The presentation is in the context of construction and solution of Mixed Integer Linear Programming (MILP) problems. We then demonstrate how this package, based on the use of CORBA interfaces for synchronous execution within a single process, can be adapted with a minimum of problem-specific changes to provide a distributed solution.

In this paper, we present an algorithm for the solution of multiparametric mixed integer linear programming (mp-MILP) problems involving (i) 0-1 integer variables, and, (ii) more than one parameter, bounded between lower and upper bounds, present on the right hand side (RHS) of constraints. The solution is approached by decomposing the mp-MILP into two subproblems and then iterating between them. The first subproblem is obtained by fixing integer variables, resulting in a multiparametric linear programming (mp-LP) problem, whereas the second subproblem is formulated as a mixed integer linear programming (MILP) problem by relaxing the parameters as variables.

Minimizing energy consumption by joint radio and computing resource allocation in Cloud-RAN

The Unmanned Aerial Vehicles (UAVs), commonly known as drones, are significant in the agriculture sphere to automate the work such as: data acquisition, crop spraying among others. This paper proposes a path planning solution for a team of UAVs that is required to whiten a greenhouse’s roof, this problem having both social-economic impact, as well as scientific-technical impact. First, the space around the roof is partitioned into cells based on a 3D cell decomposition technique, labeling the cells including the surface of the roof as regions of interest (ROIs). Based on this representation, a Petri net (PN) model captures the motion of drones, while their trajectories are returned by a Mixed Integer Linear Programming (MILP) problem which optimizes the energy consumption. An adaptable strategy is used such that the MILP problem considers only the available UAVs with enough energy to reach the ROIs. The algorithm is implemented in MATLAB and the simulation results are captured in a video link, evaluating the impact of the size of the team and the precision used for mapping the environment over the running time.

The surveillance problem is to find optimal trajectories of agents that patrol a given area as evenly as possible. In this paper, we consider multiple agents with fuel constraints. The surveillance area is given by a weighted directed graph, where the weight assigned to each arc corresponds to the fuel consumption/supply. For each node, the penalty to evaluate the unattended time is introduced. Penalties, agents, and fuels are modeled by a mixed logical dynamical system model. Then, the surveillance problem is reduced to a mixed integer linear programming (MILP) problem. Based on the policy of model predictive control, the MILP problem is solved at each discrete time. In this paper, the feasibility condition for the MILP problem is derived. Finally, the proposed method is demonstrated by a numerical example.

Knowledge representation of mathematical optimization problems and constructs for modeling

This paper aims at implementing an optimization technique in smart manufacturing applications for co-optimizing the operation of multiple production lines to supply the desired demand for multiple products. The proposed model, which is formulated as a mixed-integer linear programming (MILP) problem, minimizes the total manufacturing cost by co-optimizing the operation schedule of production lines. The proposed model implements several operating constraints of the production lines that include their energy consumption and associated costs, productivity, scrap rates, changeover duration, and labor costs. The proposed model is implemented on a sample manufacturer, where seven production lines with different operational constraints are optimized to manufacture six product types. The simulation results show that the proposed optimal scheduling model allows smart manufacturers to enhance production efficiency and save on operation costs by co-optimizing the operation schedule of production lines. Sensitivity analyses are conducted to demonstrate the performance of the model in different demand levels, where the manufacturers are enabled to optimally schedule the production lines at minimum cost given the production demand.

Research on the privacy protection of smart grids mostly stays in fixed electricity prices or electricity aggregation. Aiming at the problem of real-time pricing and privacy data protection of smart grid, this paper proposes a privacy-preserving electricity consumption statistics and billing scheme in smart grid (PPCSB). In this scheme, the techniques of additive homomorphic encryption and mixed multiplicative homomorphic encryption are used to ensure the security of the data communication; Batch verification is used to improve the efficiency of signature verification. In addition, the proposed scheme does not require a trusted third party in order to improve the usability of the scheme. Through performance analysis of the scheme, it shows that the scheme has better security and better functions.

We present a general approach to the FPGA technology mapping problem that applies to any logic block composed of lookup tables (LUTs) and can yield optimal solutions. The connections between LUTs of a logic block are modeled by virtual switches, which define a set of multiple-LUT blocks (MLBs) called an MLB-basis. We identify the MLB-bases for various commercial logic blocks. Given a n MLB-basis, we formulate FPGA mapping as a mixed integer linear programming (MILP) problem to achieve both the generality and the optimality objectives. We solve the MILP models using a general-purpose MILP solver, and present the results of mapping some ISCAS.85 benchmark circuits with a variety of commercial FPGAs. Circuits of a few hundred gates can be mapped in reasonable time using the MILP approach directly. Larger circuits can be handled by partitioning them prior to technology mapping. We show that optimal or provably near-optimal solutions can be obtained for the large ISCAS.85 benchmark circuits using partitions defined by their high-level functions.

This paper presents a metaheuristic approach to solve the transmission network expansion planning (TNEP) problem considering non-conventional solution candidates. The TNEP consists on finding the set of new elements required in a power system to meet a given future demand at a minimum cost. The TNEP traditionally considers as candidate solutions the addition of new lines and transformers. The main contribution of this work is the inclusion of non-conventional solution candidates. Such non-conventional solution candidates are namely: repowering of existing circuits and reactive shunt compensation. Also, an AC modeling of the network that allows obtaining more realistic results than the traditional DC model has been considered. The TNEP is represented by means of a non linear mixed integer programming problem which is solved through a hybrid genetic algorithm (HGA). Several tests were performed on two benchmark power systems to show the applicability and effectiveness of the proposed approach.

Predictive route choice control of destination coded vehicles with mixed integer linear programming optimization

In this paper the path planning for obstacle-avoided pursuit problem (OAP) is studied. The OAP models based on the mixed integer linear programming (MILP) is presented. In the OAP models, the dynamic equation of mass point with linear damping is taken as the state equation of vehiclepsilas motion. Integer variables are used to describe the relative position of vehicle and obstacles. ldquoExpansible Target Sizerdquo is proposed to describe the pursuit process for target step-by-step. ldquoPursuit Directionrdquo of vehicle is defined. The Isometric Plane Method selected integer variables is used to solve MILP pursuit problem. How to select the integer variables of inner point is also given. Finally, simulations are given to show the efficiency of the method.

A fundamental knowledge of the trade-off between sensor cooperation and autonomous vehicles' (AV) trajectory planning is pivotal towards characterizing the sensing capabilities of wireless sensor networks that employ AV for collecting the data and to ensure their successful integration in large scale sensor deployments. We formulate the problem of efficient data gathering as a mixed integer linear programming (MILP) problem that provides a joint optimization of AV trajectory and data-routing. Since MILP formulations are not scalable, we propose an approach to develop heuristics where the joint optimization is decoupled into three sub-problems. The first is to determine clusters of sensors with communication range limitations. The second is to efficiently connect the clusters. The third is to design the route inside the cluster that will minimize the cost of data collection. We characterize performance of the proposed heuristics through Monte-Carlo simulations. Performance is measured in terms of (a) the joint energy cost for cooperation and AV movement for different number of sensor nodes and communication ranges of these sensors, and (b) computational effort of the various heuristics. For small deployments, we compare the heuristics to the MILP global optimization and show that the gap between them can be lower than 2% for deployments as large as 18 nodes and typically below 25% for a wide range of scenarios.

Secure lightweight block ciphers have become an important aspect due to the fact that they are a popular choice for providing security in ubiquitous devices. Two of the most important attacks on block ciphers are differential cryptanalysis [1] and linear cryptanalysis [2]. Calculating the number of active S-boxes is one of the method to examine the security of block ciphers against differential attack. In this paper, we count the minimum number of active S-boxes for several rounds of the lightweight ciphers namely KLEIN, LED and AES. We utilized the method proposed in [9], where calculation of the minimum number of active S-boxes is formulated as a Mixed Integer Linear Programming (MILP) problem. The objective function is to minimize the number of active S-boxes, subject to the constraints imposed by the differential propagation of the cipher. The experimental results are presented in this paper and found to be encouraging.

In this paper we consider policies for free-flight management of air traffic. We consider instantaneous and bounded heading angle deviation as conflict avoidance maneuvers. The corresponding model, resulting in a mixed integer linear programming (MILP) problem allow to solve both conflict detection and conflict resolution problems. The developed algorithm proved successful in a centralized implementation with a large number of cooperating aircraft. However, the application of such algorithm to a free flight environment, where cooperation can only be expected from neighboring aircraft, poses many challenges. We consider a model of the decentralized conflict resolution strategy that is based on a hybrid system, and sufficient conditions under which a 3-aircraft free flight MILP-based scheme guarantees safety of flight are provided.